System

Abstract

An object of the system according to the embodiment is to easily find an organization or an activity that a user wants to support and to understand a donation method and an effect.SOLUTION: A system includes a group introduction part, a donation explanation part, and a receipt issuing part. The organization introduction unit introduces an organization or an activity that the user wants to support using the generated AI. The donation explanation part explains a method of donation to the group or activity introduced by the group introduction part and the effect thereof. The receipt issuing unit issues a receipt according to the amount of donation explained by the donation explanation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult for users to find organizations and activities they want to support, and to understand how to donate and the benefits it will bring.

[0005] The system according to the embodiment aims to enable users to easily find organizations and activities they wish to support and understand how to donate and the effects of such donations. [Means for solving the problem]

[0006] The system according to the embodiment includes an organization introduction unit, a donation explanation unit, and a receipt issuing unit. The organization introduction unit uses a generation AI to introduce organizations and activities that the user would like to support. The donation explanation unit explains how to donate to the organizations and activities introduced by the organization introduction unit and the benefits of the donation. The receipt issuing unit issues a receipt according to the donation amount explained by the donation explanation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily find organizations and activities they want to support and understand how to donate and the effects of donations. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A donation service system according to an embodiment of the present invention uses a generative AI to introduce organizations and activities that users want to support and explains how to donate and the benefits of that donation. This allows users to easily find organizations and activities they want to support and understand how to donate and the benefits of that donation.

[0029] A donation service system according to an embodiment includes a generation AI, an organization introduction unit, a donation explanation unit, and a receipt issuing unit. The generation AI accepts user instructions. For example, if a user says, "I'm interested in environmental protection," the generation AI introduces appropriate organizations and activities based on that information. The organization introduction unit presents the organizations and activities introduced by the generation AI to the user. For example, the generation AI might respond, "I recommend a nearby nonprofit organization called XX. They are working to conserve forests and oceans." The donation explanation unit explains how to donate to the introduced organizations and activities and their benefits. For example, it might explain, "By donating using PayPay, you can directly contribute to their activities. You can set the donation amount freely, but a receipt will be issued for donations of 1,000 yen or more." The receipt issuing unit issues a receipt according to the donation amount. For example, if a user donates 1,000 yen or more, the generation AI automatically issues a receipt. This makes it easier for users to understand the donation procedure and its benefits.

[0030] The organization introduction unit can learn a user's past donation history and interests and recommend personalized organizations and activities. For example, the generation AI analyzes a user's past donation history and recommends organizations and activities with similar interests. For example, a user who has donated to an animal protection organization in the past can be recommended other animal protection organizations. In addition, to learn a user's interests, the generation AI analyzes the user's social media accounts and recommends related organizations and activities. For example, a user who frequently posts about environmental protection can be recommended environmental protection organizations. In addition, the generation AI identifies interests based on a user's search history and recommends organizations and activities based on those interests. For example, a user who frequently searches for "education support" can be recommended educational support organizations. This maximizes the effectiveness of donations by recommending the most suitable organizations and activities to the user.

[0031] The organization introduction section can collect the latest information on organizations and activities in real time and provide users with the most up-to-date information. For example, the generation AI collects the latest information on organizations and activities from online news sites and social media and provides it to users. For example, it provides information on newly established NPO organizations in real time. In addition, the generation AI regularly crawls the official websites of organizations and activities to collect the latest activity reports and event information. For example, it notifies users of the latest fundraising campaign information. In addition, the generation AI connects with a database of organizations and activities via an API to obtain the latest information. For example, it provides users with information on the start of a new project. This allows users to receive the latest information and maximize the effectiveness of their donations.

[0032] The organization introduction section can use the user's geographic location information to recommend nearby organizations and activities. For example, the generation AI obtains the user's current location and recommends organizations and events active in the vicinity. For example, it can introduce charity events held near the user. The organization introduction section also recommends local organizations and activities based on the user's geographic location information. For example, it can introduce local environmental protection organizations and local NPOs. The organization introduction section also recommends nearby volunteer activities using the user's location information. For example, it can introduce cleanup activities and tree-planting events taking place near the user. This makes it possible to recommend the most suitable organizations and activities based on the user's geographic location information.

[0033] The organization introduction unit can link with the user's social media account to recommend organizations and activities supported by friends and followers. For example, the generation AI analyzes the user's social media account to recommend organizations and activities supported by friends and followers. For example, it can introduce organizations to which friends have donated. The organization introduction unit also recommends organizations and activities that are popular within the user's network based on social media data. For example, it can introduce NPOs that many followers support. The generation AI also analyzes the content of the user's social media posts to recommend organizations and activities that friends and followers are interested in. For example, it can introduce charity events shared by friends. In this way, by linking with the user's social media account, it can recommend organizations and activities supported by friends and followers.

[0034] The donation explanation unit shows the effect of donations using specific numerical values ​​and visuals, allowing the user to intuitively understand the impact of donations. For example, the generation AI in the donation explanation unit shows the effect of donations using specific numerical values, allowing the user to intuitively understand the impact of donations. For example, it displays the number of animals saved depending on the donation amount. The donation explanation unit also uses visuals to visually show the effect of donations. For example, it displays a graph showing the number of trees planted depending on the donation amount. The generation AI in the donation explanation unit also shows the effect of donations using specific examples, allowing the user to understand the impact of donations. For example, it displays a photo of a school built with donations. This allows the user to intuitively understand the effect of donations, thereby increasing their motivation to donate.

[0035] The donation explanation unit can guide the user step-by-step through the donation method, allowing the user to complete the donation without hesitation. For example, the generation AI can guide the user step-by-step through the donation method, allowing the user to complete the donation without hesitation. For example, the generation AI can provide step-by-step instructions from setting the donation amount to the payment procedure. The donation explanation unit also provides real-time support from the generation AI when the user makes a donation. For example, it can provide immediate answers to any questions that arise during the donation procedure. The donation explanation unit also provides the generation AI with videos and interactive content to explain the donation method to make it easier for the user to understand. For example, it can provide a demo video of the donation procedure. This can simplify the donation procedure by allowing the user to complete the donation without hesitation.

[0036] The donation explanation unit can propose different donation methods and provide options that meet the user's needs. For example, the generation AI in the donation explanation unit proposes different donation methods, such as regular donations and lump-sum donations, according to the user's needs. For example, a regular donation plan is proposed for a user who wishes to make a regular monthly donation. The donation explanation unit also proposes the optimal donation method based on the user's donation history. For example, a user who has made a lump-sum donation in the past is suggested to make a lump-sum donation again. The donation explanation unit also allows the generation AI to visually present options for donation methods to make it easier for the user to choose. For example, a graph comparing the benefits of regular donations and lump-sum donations is displayed. This makes it possible to simplify the donation process by providing donation methods that meet the user's needs.

[0037] The receipt issuing unit can analyze the user's past donation history and financial situation and suggest the optimal donation amount. In the receipt issuing unit, for example, the generation AI analyzes the user's past donation history and suggests the optimal donation amount. For example, it may suggest based on the average of past donation amounts. The receipt issuing unit also takes into account the user's financial situation and suggests a donation amount within a reasonable range. For example, it may calculate the optimal amount based on income and expenditure data. In addition, the receipt issuing unit also uses the generation AI to comprehensively analyze the user's donation history and financial situation and suggest the optimal donation amount. For example, it may suggest based on past donation frequency and amount. This makes it possible to maximize the impact of donations by suggesting the optimal donation amount based on the user's past donation history and financial situation.

[0038] The receipt issuing unit indicates specific effects according to the donation amount, making it easier for users to set the donation amount. For example, the generation AI in the receipt issuing unit indicates specific effects according to the donation amount, making it easier for users to set the donation amount. For example, it shows how many children will receive an education with a donation of 1,000 yen. The receipt issuing unit also uses visuals to visually show the effects according to the donation amount. For example, it displays a graph showing the number of animals saved according to the donation amount. The generation AI in the receipt issuing unit also indicates specific examples according to the donation amount, making it easier for users to set the donation amount. For example, it displays a photo of a school built with donations. This makes it easier for users to set the donation amount, simplifying the donation process.

[0039] The receipt issuing unit can suggest benefits and rewards according to the user's donation amount, providing an incentive for donations. For example, the generation AI in the receipt issuing unit suggests benefits and rewards according to the user's donation amount. For example, users who donate more than a certain amount are given a letter of appreciation or a commemorative gift. The receipt issuing unit also visually shows the benefits and rewards according to the user's donation amount. For example, it displays a list of benefits that can be received according to the donation amount. The receipt issuing unit also has the generation AI specifically explain the benefits and rewards according to the donation amount, increasing the user's motivation to donate. For example, it introduces events and tours that can be participated in according to the donation amount. In this way, it is possible to increase the user's motivation to donate by providing benefits and rewards according to the user's donation amount.

[0040] The receipt issuing unit can gamify the process of setting the donation amount, allowing the user to have fun while setting the donation amount. For example, the receipt issuing unit uses a generation AI to gamify the process of setting the donation amount, allowing the user to have fun while setting the donation amount. For example, a system may be introduced where points are earned according to the donation amount and users compete for rankings. Furthermore, when the user sets the donation amount, the generation AI provides an interface that incorporates game elements. For example, a game may be provided in which a character grows according to the donation amount. Furthermore, the receipt issuing unit uses a generation AI to gamify the process of setting the donation amount, allowing the user to have fun while setting the donation amount. For example, a system may be introduced where badges or titles are earned according to the donation amount. This allows the user to have fun while setting the donation amount, thereby increasing their motivation to donate.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The donation service system may further include a hobby / special skill recommendation unit that recommends related organizations and activities based on the user's hobbies and special skills. For example, if the user is interested in music, it may recommend organizations that support music education. The hobby / special skill recommendation unit may also explain the effects of donations related to the user's hobbies and special skills. For example, it may show how donations to music education contribute to children's development. Furthermore, the hobby / special skill recommendation unit may suggest donation methods and amounts according to the user's hobbies and special skills. For example, if the user is interested in art, it may suggest donations to art projects.

[0043] The donation service system may further include a purchase history analysis unit that analyzes the user's purchasing history and recommends related organizations and activities. For example, if the user purchases eco-friendly products, an environmental protection organization may be recommended. The purchase history analysis unit may also explain the effects of donations based on the user's purchasing history. For example, it may show how purchasing eco-friendly products contributes to environmental protection. Furthermore, the purchase history analysis unit may suggest donation methods and amounts based on the user's purchasing history. For example, if the user purchases organic food, it may suggest donations to an organization that supports organic agriculture.

[0044] The donation service system may further include a life event recommendation unit that recommends relevant organizations and activities based on the user's life events. For example, if the user gets married, organizations and activities related to marriage are recommended. The life event recommendation unit may also explain the effects of donations based on the user's life events. For example, it may show how donations related to marriage can support a newlywed family. Furthermore, the life event recommendation unit may suggest donation methods and amounts according to the user's life events. For example, if the user has a child, it may suggest donations to organizations that support children's education.

[0045] The donation service system may further include an occupation recommendation unit that recommends related organizations and activities based on the user's occupation. For example, if the user is a medical professional, it may recommend a medical support organization. The occupation recommendation unit may also explain the effects of donations based on the user's occupation. For example, it may show how donations to medical support organizations support medical facilities. Furthermore, the occupation recommendation unit may suggest donation methods and amounts according to the user's occupation. For example, if the user is an educator, it may suggest donations to educational support organizations.

[0046] The donation service system may further include a family structure recommendation unit that recommends relevant organizations and activities based on the user's family structure. For example, if the user has children, it may recommend children's welfare organizations. The family structure recommendation unit may also explain the effects of donations based on the user's family structure. For example, it may show how donating to children's welfare organizations will improve the lives of children. The family structure recommendation unit may also suggest donation methods and amounts according to the user's family structure. For example, if the user is caring for elderly people, it may suggest donations to elderly support organizations.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The organization introduction section uses the generation AI to introduce organizations and activities that the user wants to support. For example, if the user says, "I'm interested in environmental protection," the generation AI will use that information to introduce appropriate organizations and activities, and the organization introduction section will present that information to the user. Specifically, the introduction might be something like, "I recommend a nearby NPO called XX. They are working to conserve forests and oceans." Step 2: The Donation Explanation Department explains how to donate to the organizations and activities introduced by the Organization Introduction Department and the benefits of the donation. For example, they might explain, "By donating using PayPay, you can directly contribute to their activities. You can set the donation amount as you like, but a receipt will be issued for donations of 1,000 yen or more." Step 3: The receipt issuing unit issues a receipt according to the donation amount explained by the donation explanation unit. For example, if the user donates 1,000 yen or more, the generation AI automatically issues a receipt.

[0049] (Example 2) A donation service system according to an embodiment of the present invention uses a generative AI to introduce organizations and activities that users want to support and explains how to donate and the benefits of that donation. This allows users to easily find organizations and activities they want to support and understand how to donate and the benefits of that donation.

[0050] A donation service system according to an embodiment includes a generation AI, an organization introduction unit, a donation explanation unit, and a receipt issuing unit. The generation AI accepts user instructions. For example, if a user says, "I'm interested in environmental protection," the generation AI introduces appropriate organizations and activities based on that information. The organization introduction unit presents the organizations and activities introduced by the generation AI to the user. For example, the generation AI might respond, "I recommend a nearby nonprofit organization called XX. They are working to conserve forests and oceans." The donation explanation unit explains how to donate to the introduced organizations and activities and their benefits. For example, it might explain, "By donating using PayPay, you can directly contribute to their activities. You can set the donation amount freely, but a receipt will be issued for donations of 1,000 yen or more." The receipt issuing unit issues a receipt according to the donation amount. For example, if a user donates 1,000 yen or more, the generation AI automatically issues a receipt. This makes it easier for users to understand the donation procedure and its benefits.

[0051] The organization introduction unit can learn a user's past donation history and interests and recommend personalized organizations and activities. For example, the generation AI analyzes a user's past donation history and recommends organizations and activities with similar interests. For example, a user who has donated to an animal protection organization in the past can be recommended other animal protection organizations. In addition, to learn a user's interests, the generation AI analyzes the user's social media accounts and recommends related organizations and activities. For example, a user who frequently posts about environmental protection can be recommended environmental protection organizations. In addition, the generation AI identifies interests based on a user's search history and recommends organizations and activities based on those interests. For example, a user who frequently searches for "education support" can be recommended educational support organizations. This maximizes the effectiveness of donations by recommending the most suitable organizations and activities to the user.

[0052] The organization introduction section can collect the latest information on organizations and activities in real time and provide users with the most up-to-date information. For example, the generation AI collects the latest information on organizations and activities from online news sites and social media and provides it to users. For example, it provides information on newly established NPO organizations in real time. In addition, the generation AI regularly crawls the official websites of organizations and activities to collect the latest activity reports and event information. For example, it notifies users of the latest fundraising campaign information. In addition, the generation AI connects with a database of organizations and activities via an API to obtain the latest information. For example, it provides users with information on the start of a new project. This allows users to receive the latest information and maximize the effectiveness of their donations.

[0053] The organization introduction unit can use the emotion estimation function to analyze the user's emotional state and recommend organizations and activities that correspond to the emotion. For example, the organization introduction unit uses a generation AI to analyze the user's emotional state at the time of input in real time and recommend organizations and activities that elicit positive emotions. For example, if the user is feeling stressed, it recommends relaxation activities. The organization introduction unit also uses the emotion estimation function to analyze the emotions the user feels when making a donation and recommends organizations and activities that correspond to the emotion. For example, if the user is moved, it recommends organizations that can share that emotion. The organization introduction unit also builds a system that recommends organizations and activities that correspond to the user's emotions based on the user's emotional data. For example, if the user is sad, it recommends activities that will comfort them. This allows the effectiveness of donations to be maximized by recommending organizations and activities that correspond to the user's emotions.

[0054] The organization introduction section can use the user's geographic location information to recommend nearby organizations and activities. For example, the generation AI obtains the user's current location and recommends organizations and events active in the vicinity. For example, it can introduce charity events held near the user. The organization introduction section also recommends local organizations and activities based on the user's geographic location information. For example, it can introduce local environmental protection organizations and local NPOs. The organization introduction section also recommends nearby volunteer activities using the user's location information. For example, it can introduce cleanup activities and tree-planting events taking place near the user. This makes it possible to recommend the most suitable organizations and activities based on the user's geographic location information.

[0055] The organization introduction unit can link with the user's social media account to recommend organizations and activities supported by friends and followers. For example, the generation AI analyzes the user's social media account to recommend organizations and activities supported by friends and followers. For example, it can introduce organizations to which friends have donated. The organization introduction unit also recommends organizations and activities that are popular within the user's network based on social media data. For example, it can introduce NPOs that many followers support. The generation AI also analyzes the content of the user's social media posts to recommend organizations and activities that friends and followers are interested in. For example, it can introduce charity events shared by friends. In this way, by linking with the user's social media account, it can recommend organizations and activities supported by friends and followers.

[0056] The donation explanation unit shows the effect of donations using specific numerical values ​​and visuals, allowing the user to intuitively understand the impact of donations. For example, the generation AI in the donation explanation unit shows the effect of donations using specific numerical values, allowing the user to intuitively understand the impact of donations. For example, it displays the number of animals saved depending on the donation amount. The donation explanation unit also uses visuals to visually show the effect of donations. For example, it displays a graph showing the number of trees planted depending on the donation amount. The generation AI in the donation explanation unit also shows the effect of donations using specific examples, allowing the user to understand the impact of donations. For example, it displays a photo of a school built with donations. This allows the user to intuitively understand the effect of donations, thereby increasing their motivation to donate.

[0057] The donation explanation unit can guide the user step-by-step through the donation method, allowing the user to complete the donation without hesitation. For example, the generation AI can guide the user step-by-step through the donation method, allowing the user to complete the donation without hesitation. For example, the generation AI can provide step-by-step instructions from setting the donation amount to the payment procedure. The donation explanation unit also provides real-time support from the generation AI when the user makes a donation. For example, it can provide immediate answers to any questions that arise during the donation procedure. The donation explanation unit also provides the generation AI with videos and interactive content to explain the donation method to make it easier for the user to understand. For example, it can provide a demo video of the donation procedure. This can simplify the donation procedure by allowing the user to complete the donation without hesitation.

[0058] The donation explanation unit uses the emotion estimation function to emphasize the effects of donations according to the user's emotions, thereby increasing motivation to donate. The donation explanation unit, for example, uses the emotion estimation function to emphasize the effects of donations according to the user's emotions. For example, if the user is moved, the effect of being able to share the emotion is emphasized. The donation explanation unit also builds a system that emphasizes the effects of donations according to the user's emotions based on the user's emotion data. For example, if the user is sad, the comforting effect is emphasized. The donation explanation unit also uses a generation AI to analyze the user's emotional state in real time and emphasize the effects of donations according to the emotion. For example, if the user is feeling stressed, the relaxation effect is emphasized. In this way, motivation to donate can be increased by emphasizing the effects of donations according to the user's emotions.

[0059] The donation explanation unit can propose different donation methods and provide options that meet the user's needs. For example, the generation AI in the donation explanation unit proposes different donation methods, such as regular donations and lump-sum donations, according to the user's needs. For example, a regular donation plan is proposed for a user who wishes to make a regular monthly donation. The donation explanation unit also proposes the optimal donation method based on the user's donation history. For example, a user who has made a lump-sum donation in the past is suggested to make a lump-sum donation again. The donation explanation unit also allows the generation AI to visually present options for donation methods to make it easier for the user to choose. For example, a graph comparing the benefits of regular donations and lump-sum donations is displayed. This makes it possible to simplify the donation process by providing donation methods that meet the user's needs.

[0060] The donation explanation unit uses the emotion estimation function to analyze the emotional response of the user when hearing about the effects of donations, and can emphasize the effect that resonates most emotionally. For example, the donation explanation unit uses the emotion estimation function to analyze the emotional response of the user when hearing about the effects of donations in real time, and emphasize the effect that resonates most emotionally. For example, if the user is moved, the effect of being able to share that emotion is emphasized. The donation explanation unit also builds a system that emphasizes the effects of donations according to the user's emotions, based on the user's emotional data. For example, if the user is sad, the comforting effect is emphasized. The donation explanation unit also uses a generation AI to analyze the user's emotional state in real time, and emphasize the effects of donations according to the emotion. For example, if the user is feeling stressed, the relaxation effect is emphasized. In this way, by emphasizing the effects of donations based on the user's emotional responses, it is possible to increase the user's motivation to donate.

[0061] The receipt issuing unit can analyze the user's past donation history and financial situation and suggest the optimal donation amount. In the receipt issuing unit, for example, the generation AI analyzes the user's past donation history and suggests the optimal donation amount. For example, it may suggest based on the average of past donation amounts. The receipt issuing unit also takes into account the user's financial situation and suggests a donation amount within a reasonable range. For example, it may calculate the optimal amount based on income and expenditure data. In addition, the receipt issuing unit also uses the generation AI to comprehensively analyze the user's donation history and financial situation and suggest the optimal donation amount. For example, it may suggest based on past donation frequency and amount. This makes it possible to maximize the impact of donations by suggesting the optimal donation amount based on the user's past donation history and financial situation.

[0062] The receipt issuing unit indicates specific effects according to the donation amount, making it easier for users to set the donation amount. For example, the generation AI in the receipt issuing unit indicates specific effects according to the donation amount, making it easier for users to set the donation amount. For example, it shows how many children will receive an education with a donation of 1,000 yen. The receipt issuing unit also uses visuals to visually show the effects according to the donation amount. For example, it displays a graph showing the number of animals saved according to the donation amount. The generation AI in the receipt issuing unit also indicates specific examples according to the donation amount, making it easier for users to set the donation amount. For example, it displays a photo of a school built with donations. This makes it easier for users to set the donation amount, simplifying the donation process.

[0063] The receipt issuing unit uses the emotion estimation function to suggest a donation amount according to the user's emotion, thereby increasing the user's motivation to donate. The receipt issuing unit, for example, uses the emotion estimation function to suggest a donation amount according to the user's emotion. For example, if the user is moved, the receipt issuing unit suggests an amount that will allow the user to share that emotion. The receipt issuing unit also builds a system that suggests a donation amount according to the user's emotion based on the user's emotion data. For example, if the user is sad, the receipt issuing unit suggests an amount that will comfort the user. The receipt issuing unit also uses a generation AI to analyze the user's emotional state in real time and suggest a donation amount according to the emotion. For example, if the user is feeling stressed, the receipt issuing unit suggests an amount that will have a relaxing effect. In this way, the user's motivation to donate can be increased by suggesting a donation amount according to the user's emotion.

[0064] The receipt issuing unit can suggest benefits and rewards according to the user's donation amount, providing an incentive for donations. For example, the generation AI in the receipt issuing unit suggests benefits and rewards according to the user's donation amount. For example, users who donate more than a certain amount are given a letter of appreciation or a commemorative gift. The receipt issuing unit also visually shows the benefits and rewards according to the user's donation amount. For example, it displays a list of benefits that can be received according to the donation amount. The receipt issuing unit also has the generation AI specifically explain the benefits and rewards according to the donation amount, increasing the user's motivation to donate. For example, it introduces events and tours that can be participated in according to the donation amount. In this way, it is possible to increase the user's motivation to donate by providing benefits and rewards according to the user's donation amount.

[0065] The receipt issuing unit can gamify the process of setting the donation amount, allowing the user to have fun while setting the donation amount. For example, the receipt issuing unit uses a generation AI to gamify the process of setting the donation amount, allowing the user to have fun while setting the donation amount. For example, a system may be introduced where points are earned according to the donation amount and users compete for rankings. Furthermore, when the user sets the donation amount, the generation AI provides an interface that incorporates game elements. For example, a game may be provided in which a character grows according to the donation amount. Furthermore, the receipt issuing unit uses a generation AI to gamify the process of setting the donation amount, allowing the user to have fun while setting the donation amount. For example, a system may be introduced where badges or titles are earned according to the donation amount. This allows the user to have fun while setting the donation amount, thereby increasing their motivation to donate.

[0066] The receipt issuing unit can use the emotion estimation function to analyze the emotion a user feels when setting a donation amount and suggest the amount that will elicit the most positive emotion. For example, the receipt issuing unit can use the emotion estimation function to analyze the emotion a user feels in real time when setting a donation amount and suggest the amount that will elicit the most positive emotion. For example, if the user is moved, the receipt issuing unit suggests an amount that will allow the user to share that emotion. The receipt issuing unit also builds a system that suggests a donation amount based on the user's emotion data. For example, if the user is sad, the receipt issuing unit suggests an amount that will comfort the user. The receipt issuing unit also uses a generation AI to analyze the user's emotional state in real time and suggest a donation amount that is based on the emotion. For example, if the user is feeling stressed, the receipt issuing unit suggests an amount that will have a relaxing effect. In this way, suggesting a donation amount that is based on the user's emotion can increase the user's motivation to donate.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The donation service system may further include a health monitoring unit that monitors the user's health status and recommends health-related organizations and activities. For example, if the user uses a fitness tracker, the health monitoring unit analyzes the data and recommends organizations and activities that contribute to health promotion. The health monitoring unit may also explain the effects of health-related donations based on the user's health status. For example, the health monitoring unit may show how donations can help provide health programs. Furthermore, the health monitoring unit may suggest the timing and amount of donations depending on the user's health status. For example, if the user is feeling stressed, the health monitoring unit may suggest donating to relaxation activities.

[0069] The donation service system may further include a hobby / special skill recommendation unit that recommends related organizations and activities based on the user's hobbies and special skills. For example, if the user is interested in music, it may recommend organizations that support music education. The hobby / special skill recommendation unit may also explain the effects of donations related to the user's hobbies and special skills. For example, it may show how donations to music education contribute to children's development. Furthermore, the hobby / special skill recommendation unit may suggest donation methods and amounts according to the user's hobbies and special skills. For example, if the user is interested in art, it may suggest donations to art projects.

[0070] The donation service system may further include a purchase history analysis unit that analyzes the user's purchasing history and recommends related organizations and activities. For example, if the user purchases eco-friendly products, an environmental protection organization may be recommended. The purchase history analysis unit may also explain the effects of donations based on the user's purchasing history. For example, it may show how purchasing eco-friendly products contributes to environmental protection. Furthermore, the purchase history analysis unit may suggest donation methods and amounts based on the user's purchasing history. For example, if the user purchases organic food, it may suggest donations to an organization that supports organic agriculture.

[0071] The donation service system may further include an emotion timing suggestion unit that estimates the user's emotions and suggests the timing of donations according to the emotions. For example, if the user is emotional, the emotion timing suggestion unit suggests donations at the timing when the user's emotions are at their peak. The emotion timing suggestion unit may also emphasize the effect of donations based on the user's emotions. For example, for an emotional user, the emotion timing suggestion unit may emphasize the effect of sharing the emotion. The emotion timing suggestion unit may also suggest donation methods and amounts according to the user's emotions. For example, if the user is feeling stressed, the emotion timing suggestion unit may suggest donations for relaxation activities.

[0072] The donation service system may further include a life event recommendation unit that recommends relevant organizations and activities based on the user's life events. For example, if the user gets married, organizations and activities related to marriage are recommended. The life event recommendation unit may also explain the effects of donations based on the user's life events. For example, it may show how donations related to marriage can support a newlywed family. Furthermore, the life event recommendation unit may suggest donation methods and amounts according to the user's life events. For example, if the user has a child, it may suggest donations to organizations that support children's education.

[0073] The donation service system may further include an occupation recommendation unit that recommends related organizations and activities based on the user's occupation. For example, if the user is a medical professional, it may recommend a medical support organization. The occupation recommendation unit may also explain the effects of donations based on the user's occupation. For example, it may show how donations to medical support organizations support medical facilities. Furthermore, the occupation recommendation unit may suggest donation methods and amounts according to the user's occupation. For example, if the user is an educator, it may suggest donations to educational support organizations.

[0074] The donation service system may further include a family structure recommendation unit that recommends relevant organizations and activities based on the user's family structure. For example, if the user has children, it may recommend children's welfare organizations. The family structure recommendation unit may also explain the effects of donations based on the user's family structure. For example, it may show how donating to children's welfare organizations will improve the lives of children. The family structure recommendation unit may also suggest donation methods and amounts according to the user's family structure. For example, if the user is caring for elderly people, it may suggest donations to elderly support organizations.

[0075] The donation service system may further include an emotional reward suggestion unit that estimates the user's emotions and suggests donation rewards according to the emotions. For example, if the user is emotional, the emotional reward suggestion unit suggests rewards that allow the user to share their emotions. The emotional reward suggestion unit may also emphasize the effect of donations based on the user's emotions. For example, for an emotionally moved user, the emotional reward suggestion unit may emphasize the effect of sharing their emotions. The emotional reward suggestion unit may also suggest donation methods and amounts according to the user's emotions. For example, if the user is feeling stressed, the emotional reward suggestion unit may suggest rewards that have a relaxation effect.

[0076] The donation service system may further include an emotion incentive providing unit that estimates the user's emotion and provides a donation incentive according to the emotion. For example, if the user is moved, an incentive that allows the user to share the emotion is provided. The emotion incentive providing unit may also emphasize the effect of donation based on the user's emotion. For example, for a user who is moved, the effect of being able to share the emotion is emphasized. Furthermore, the emotion incentive providing unit may suggest donation methods and amounts according to the user's emotion. For example, if the user is feeling stressed, an incentive that has a relaxation effect is provided.

[0077] The donation service system may further include an emotion feedback providing unit that estimates the user's emotion and provides donation feedback according to the emotion. For example, if the user is moved, feedback that allows the user to share the emotion is provided. The emotion feedback providing unit may also emphasize the effect of donation based on the user's emotion. For example, for a user who is moved, the effect of sharing the emotion is emphasized. The emotion feedback providing unit may also suggest donation methods and amounts according to the user's emotion. For example, if the user is feeling stressed, feedback that has a relaxation effect is provided.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The organization introduction section uses the generation AI to introduce organizations and activities that the user wants to support. For example, if the user says, "I'm interested in environmental protection," the generation AI will use that information to introduce appropriate organizations and activities, and the organization introduction section will present that information to the user. Specifically, the introduction might be something like, "I recommend a nearby NPO called XX. They are working to conserve forests and oceans." Step 2: The Donation Explanation Department explains how to donate to the organizations and activities introduced by the Organization Introduction Department and the benefits of the donation. For example, they might explain, "By donating using PayPay, you can directly contribute to their activities. You can set the donation amount as you like, but a receipt will be issued for donations of 1,000 yen or more." Step 3: The receipt issuing unit issues a receipt according to the donation amount explained by the donation explanation unit. For example, if the user donates 1,000 yen or more, the generation AI automatically issues a receipt.

[0080] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0086] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0087] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0088] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0089] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0090] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0121] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0130] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0131] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0132] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0133] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0135] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0136] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0137] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0138] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0139] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0140] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0141] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0142] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0143] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0144] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An organization introduction section that uses generative AI to introduce organizations and activities that users want to support, and a donation explanation section that explains how to donate to the organizations and activities introduced by the organization introduction section and the effects of the donation; a receipt issuing unit that issues a receipt according to the donation amount explained by the donation explanation unit. A system characterized by:

2. The organization introduction department: Learn about the user's past donation history and interests to recommend personalized organizations and activities 2. The system of claim 1.

3. The organization introduction department: Collect the latest information on organizations and activities in real time and provide the latest information to the user.

2. The system of claim 1.

4. The organization introduction department: Analyzing the user's emotional state and recommending organizations and activities according to the emotion 2. The system of claim 1.

5. The organization introduction department: Use the user's geographic location to recommend nearby organizations and activities 2. The system of claim 1.

6. The organization introduction department: Linking with the user's social media accounts to recommend organizations and causes supported by friends and followers 2. The system of claim 1.

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